tag:blogger.com,1999:blog-18206410.post7756449891242249121..comments2013-04-16T13:16:48.244-05:00Comments on Haunted by randomness: Stochasticity, Randomness and UncertaintyS. Phil Kimhttps://plus.google.com/103566929111600492508noreply@blogger.comBlogger2125tag:blogger.com,1999:blog-18206410.post-518005847143331012013-04-07T17:52:16.648-05:002013-04-07T17:52:16.648-05:00In my experience, we in OR do distinguish "ri...In my experience, we in OR do distinguish "risk" and "uncertainty". "Risk" is associated with parameters whose value may be unknown, but whose distribution is known. This is the realm of stochastic programming. A common stochastic programming objective is to maximize an expected value subject to constraints. "Uncertainty" is when parameters have values that are unknown and do not come from known distributions. That is the realm of robust optimization. A common robust optimization objective is to get the best outcome in the face of the worst-case realization of the parameter values.<br /><br />Note that almost any deterministic model has analogs that involve risk or uncertainty. Many, many of these models are of real-world interest. So in general, I would say that stochastic and robust models are harder than deterministic ones. But deterministic ones can certainly be hard enough.Matthew Saltzmanhttps://www.blogger.com/profile/03225420623527632062noreply@blogger.comtag:blogger.com,1999:blog-18206410.post-43787635521194908212013-04-07T15:13:04.943-05:002013-04-07T15:13:04.943-05:00I suspect that the people working with fuzzy sets ...I suspect that the people working with fuzzy sets and fuzzy logic (in the mathematical sense, not the congressional sense) would consider "uncertain" and "random" to be quite different.Paul Rubinhttps://www.blogger.com/profile/05801891157261357482noreply@blogger.com